Location sharing social services are popular among mobile users resulting in a huge social dataset available for researchers to explore. In this paper we consider location sharing social services’ APIs endpoints as “social sensors” that provide data revealing real world interactions, although in some cases, the number of recorded social data can be several orders of magnitude lower compared to the number of real world interactions. In the presented work we focus on check-ins at airports performing two experiments: one analyzing check-in data collected exclusively from Foursquare and another collecting additionally check-in data from Facebook. We compare the two popular location sharing social platforms’ check-ins and we show that for the case of Foursquare these data can be indicative of the passengers’ traffic, while their number is hundreds of times lower than the number of actual traffic observations.

One popular and widely use of augmented reality based application, is the projection of points of interests on top of the phones’ camera view. In this paper we discuss the implementation of an AR application that acts as a magic lens over printed maps, overlaying POIs and routes. This method expands the information space available to members of groups during navigation, partially mitigating the issue of several group members trying to share a small screen device. Our work complements existing literature by focusing on the navigation tasks and by using self-reporting questionnaires to measure affective state and user experience. We evaluate this system with groups of real tourists in a pre- liminary field trial and report our findings.

Social Networking Sites (SNS) are used daily by billions of people worldwide to keep them informed about the latest news, to help them interact with other people as well as to provide them with Points of Interest (POIs) to visit. In this paper we examine to what extent the information from SNSs such as likes, tags, check- ins can influence the visitors or locals of a city in choosing venues to visit. Next, we implement an Android application, Social City, for mobile devices, which collects and evaluates the information from Facebook and Foursquare in order to recommend to users venues to visit in the city of Patras, Greece. Finally, we discuss an evaluation of Social City. Our results indicate that the combination of SNS data from multiple social networking sites into a single rating, appears to lead to more efficient recommendations for the users, helping them choose faster and easier and with more confidence about the quality of their choice.

The retrieval of the appropriate contact in order to start a new communication session from the contact repository of mobile devices can be a time consuming procedure since mobile contact lists usually contain hundreds of items. Several researchers have focused in the past on predicting the next contact a user is likely to call, a task that could prove useful in designing adaptive context-aware interfaces for the mobile contact list. Most of the researchers propose several contextual dimensions that could be used to predict the next callee, location being one of them. However, none of these research works have ever examined the impact of location on mobile communications and only few have actually incorporated this contextual dimension on their implementations. In this paper, we examine physical location as a contextual cue for adaptive mobile contact lists by analyzing call logs from the Nokia Mobile Data Challenge dataset. Our work indicates that, contrary to previous literature, the consideration of physical location as a context dimension does not necessarily lead to improvements in the accuracy of predicting the likelihood of communication with contacts for all types of users included in the dataset under review. Finally, we also discuss the possible reasons behind this limited impact.

Through our participatory design with older adults a theme of error support for texting on smartphones emerged. Here we present the MaxieKeyboard based on the outcomes from this process. The keyboard highlights errors, autocorrections and suggestion bar usage in the composition area and gives feedback on the keyboard on typing correctness. Our older adults groups have shown strong support for the keyboard.